Teaching

Teaching

Winter term 2025/2026

Seminar where recent deep learning papers are presented and discussed.

Alexander Ecker, Nina Nellen, Felix Benjamin Müller

Summer term 2025

Practical course on applying deep learning for image generation.

Alexander Ecker and Timo Lüddecke

Introduction to Machine Learning

Alexander Ecker

Winter term 2024/2025

Seminar where recent deep learning papers are presented and discussed.

Alexander Ecker, Michaela Vystrčilová, Felix Benjamin Müller

Introduction to Graph Machine Learning

Martin Ritzert, Alexander Ecker, and Felix Müller

Bachelor’s and Master’s theses

General requirements

If you’re interested in joining our lab for a thesis, have a look at this document describing how we work and what we expect from you.

We expect prospective students to have substantial knowledge in machine learning, its mathematical foundations and Python programming. We therefore expect students interested in doing their thesis in our lab to take our courses on Machine Learning and Deep Learning for Computer Vision unless they have acquired equivalent knowledge otherwise. For Bachelor’s students, we also recommend the Practical Course Data Science.

Further recommended courses are:

  • M.Inf.2241: Current Topics in Machine Learning (seminar)
  • M.Inf.2541: Current Topics in Computational Neuroscience (seminar)
  • M.Inf.2242: Journal Club Machine Learning and Computational Neuroscience (can be done in parallel to master’s thesis)
  • M.Inf.2201: Probabilistic Machine Learning (by Fabian Sinz)
  • B.Inf.1240: Visualization (by Bernhard Schmitzer)
  • B.Phy.5601: Theoretical and Computational Neuroscience I
  • B.Phy.5602: Theoretical and Computational Neuroscience II
  • M.Psy.901: From Vision to Action
  • B.Psy.902: Biologische Psychologie: Neurowissenschaften
  • B.Phy.5676: Computer Vision and Robotics

Please note, our thesis supervision capacity is limited and we receive more thesis inquiries than we are able supervise. Therefore, we have to select candidates. If you are interested, please write an email with the subject “Master’s thesis” or “Bachelor’s thesis” containing one to three sentences about what you would like to work on and your study record to the supervisor stated below.

We will get back to you within a few days. Otherwise, do not hesitate to remind us :).

Thesis offers

3D Reconstruction of a Scarred Left Ventricle
Segmentation of Left Ventricle and Scar Tissue from MRI and Mapping onto a 3D Model
Supervisor: Ina Braun
Algorithmic Association Methods for Multi-Object Tracking
Using classical graph algorithms to associate objects over time
Supervisor: Jan Frederik Meier
Analysis of Muscle Fiber Structure in Cardiac Tissue
Comparison of machine learning and physics based methods
Supervisor: Ina Braun
Approach 2D Multi-Object Tracking in 3D
Using depth estimation to resolve ambigious scenarios
Supervisor: Jan Frederik Meier
Create benchmark dataset for hierarchical clustering
Create embeddings in hyperbolic space to have a dataset with innate unarguable hierarchy
Supervisor: Polina Turishcheva & Chase van de Geijn
Embeddings for neurons function and how they relate to morphology and cell types
Improve functional neuronal clustering
Supervisor: Polina Turishcheva
Few-Shot Object Detection for the Primate Domain
Adapt zero-shot detectors with few labeled images
Supervisor: Jan Frederik Meier
Implicit learning for neuronal prepresentation
Improve functional neuronal clustering
Supervisor: Polina Turishcheva
Mamba-inspired model for neuron-to-neuron architecture
Improve functional neuronal clustering
Supervisor: Polina Turishcheva
Model Neurons Interactions in time and between each other
Adjust readouts for neuroscience vision models to consider time and neurons interactions
Supervisor: Polina Turishcheva
Multiplexed traveling waves for encoding sequence position in State-Space models
Traveling Waves in Mamba
Supervisor: Chase van de Geijn
Probabilistic hierarchical clustering
Merge t-mixture models overclustercluster and merge method with hierarchical mixtures of Gaussians
Supervisor: Polina Turishcheva
Neural Data Science Group
Institute of Computer Science
University of Goettingen